Distortion-aware multiple input multiple output precoding

ABSTRACT

Precoding parameters used for precoding of a source are selected to minimize distortion that would otherwise be induced in the source during encoding and transmission of the source over a multiple input multiple output (MIMO) channel.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of, claims the benefit of andpriority to previously filed U.S. patent application Ser. No. 14/021,194filed Sep. 9, 2013, which is a continuation of U.S. patent applicationSer. No. 12/800,265, filed May 12, 2010, which is a continuation-in-partof U.S. patent application Ser. No. 12/655,091, filed Dec. 23, 2009. Allof the above are incorporated herein by reference in their entirety.

BACKGROUND

Wireless communication technology has evolved from a technology offeringmainly voice service to a technology that also provides multimediacontent. Recent advances in mobile computing and wireless communicationsenable transmission of rich multimedia content over wireless networks.One such advance is the use of MIMO (Multiple Input Multiple Output)communications in which multiple antennas are used at both thetransmitter and the receiver for increasing data throughput withoutrequiring additional bandwidth. Further, while MIMO configurations areusually optimized to maximize data transmission rates, with theincreased demand for various different services at the applicationlayer, achieving high reliability in addition to high data transmissionrates at the physical layer (PHY) has become ever more important.However, high data rates and high reliability tend to be conflictingdesign parameters.

Typical wireless communications involve the transmission of a continuoussource over a noisy channel. Common examples are speech communications,multimedia communications, mobile TV, mobile video and broadcaststreaming. In such communications, the source is encoded and compressedinto a finite stream of bits, and the bit stream is then communicatedover the noisy channel. Source coding is carried out to convert thecontinuous source into a finite stream of bits, and channel coding isperformed before transmission to mitigate the errors in the bit streamthat will be introduced by the noisy channel. At the receiver end, achannel decoder recovers the bit stream from its noisy version, and asource decoder reconstructs the multimedia source from the recoveredcompressed version. During transmission of a multimedia communication,minimizing distortion between the original multimedia source and thereconstructed version at the receiver can provide a better multimediaexperience for a user.

BRIEF DESCRIPTION OF THE DRAWINGS

The detailed description is set forth with reference to the accompanyingdrawing figures. In the figures, the left-most digit(s) of a referencenumber identifies the figure in which the reference number firstappears. The use of the same reference numbers in different figuresindicates similar or identical items or features.

FIG. 1 depicts a block diagram of an example distortion-awarecommunication system according to some implementations disclosed herein.

FIG. 2 depicts a flow diagram of an example process for distortion-awarecommunications according to some implementations.

FIG. 3 depicts a block diagram of an example open loop system accordingto some implementations.

FIG. 4 depicts a block diagram of an example closed loop systemaccording to some implementations.

FIG. 5 depicts a flow diagram of an example process for open-loopdistortion-aware communications according to some implementations.

FIG. 6 depicts a flow diagram of an example process for closed-loopdistortion-aware communications according to some implementations.

FIG. 7 depicts a flow diagram of an example process for determining linkadaptation parameters according to some implementations.

FIG. 8 depicts a block diagram of an example distortion-awarecommunication system according to some implementations.

DETAILED DESCRIPTION

Distortion-Aware Link Adaptation with Precoding

Some implementations herein provide a distortion-aware MIMO (MultipleInput Multiple Output) communication system that minimizes end-to-enddistortion of transmissions. For example, some implementations providefor MIMO link adaptation for enhancing multimedia communications andoptimizing end-to-end robustness of multimedia content delivery in orderto provide a multimedia experience with less distortion thanconventional systems. Some implementations herein present designcriteria for selecting MIMO precoding parameters in order to minimizeend-to-end distortion through distortion-aware precoding design.Precoding according to these implementations is a processing techniquethat exploits channel state information (CSI) by operating on the signalbefore transmission. Subject to power constraints, precoding may beperformed to ensure that the transmit signal has a desirable structure(e.g., covariance) to optimize certain performance metrics. Typically,these metrics have included capacity, pair-wise error probability,symbol error rate and signal-to-noise ratio (SNR). Implementationsherein provide a framework for an optimal precoding design in order tosupport distortion-aware link adaptation techniques.

During precoding some implementations select a beamforming matrix fororthogonal transmit beam directions and power allocation across thesebeams based on distortion minimizing considerations. Thus, thedistortion-aware arrangements herein can provide enhanced multimediacommunications for optimizing end-to-end robustness of multimediacontent (e.g., mobile video) delivery in order to enable an improveduser experience. For example, based on distortion-minimizing selectioncriteria, implementations provide “distortion-aware” precodingguidelines for selecting power allocation strategies across MIMOtransmit beams for different kinds of statistical CSI (e.g., channelmean, channel correlation structure, and the like). Consequently,implementations herein provide for adaptive modulation and coding (AMC),MIMO space-time modulation, rate/power adaptation, precoding and antennaselection techniques subject to one or more end-to-end distortionminimization criteria.

FIG. 1 illustrates an example block diagram of a communication system100 according to some implementations herein. System 100 includes adistortion-aware transmitter 102 able to communicate with a receiver 104through a MIMO channel 106. The distortion-aware transmitter 102 isconfigured to receive a source to be transmitted 108. A distortion-awareencoding component 110 at the distortion-aware transmitter 102 encodesthe source prior to transmission based on distortion minimizingconsiderations. Thus, the transmitter 102 is able to take into accountdistortion minimizing link adaptation parameters and then transmit theencoded source over the MIMO channel to the receiver 104. The receiver104 is configured to receive the MIMO transmission and reconstruct thetransmission to generate a transmitted reconstructed source 112.

Because the distortion-aware transmitter 102 takes distortion minimizingparameters into consideration during encoding of the source, the systemis able to achieve minimized end-to-end distortion 114 between thesource to be transmitted 108 and the transmitted reconstructed source112, thereby providing improved communications for transmittingmultimedia items and the like. Optionally, the receiver 104 may also bedistortion-aware and provide feedback to the transmitter 102 forenabling the transmitter 102 to be distortion aware. For example, thereceiver 104 may determine precoding criteria and other link adaptationparameters to minimize end-to-end distortion. The receiver 104 canprovide these parameters as feedback to the transmitter 102, which thenuses the provided parameters. In this optional configuration,distortion-aware transmitter 102 may also send the rate-distortioncharacteristics of the source to receiver 104, so that thedistortion-aware receiver 104 can utilize this information indetermining the precoding and other link adaptation parameters toachieve the minimized end-to-end distortion 114.

FIG. 2 illustrates a flow diagram of an example process 200corresponding to the implementation of FIG. 1. In the flow diagram, theoperations are summarized in individual blocks. The operations may beperformed in hardware, or as processor-executable instructions (softwareor firmware) that may be executed by one or more processors. Further,the process 200 may, but need not necessarily, be implemented using thesystem of FIG. 1. Consequently, by way of explanation, and notlimitation, the process 200 is described in the context of the system ofFIG. 1.

At block 202, a source is provided to a transmitter for transmission.For example, the source may be a continuous or finite source, such as amultimedia communication, e.g., voice over IP, speech and audiocommunications, mobile TV, mobile video services, or the like.Implementations herein may apply to multimedia communications overwireless local area networks (WLANs), wireless personal area networks(WPANs), wireless wide area networks (WWANs) and wireless metropolitanarea networks (WMANs). Moreover, implementations may include cellularnetworks, mobile broadband networks, satellite broadcasting systems andterrestrial broadcasting systems. For example, implementations can beused in 802.11-based LANs (e.g., the IEEE 802.11 standard, IEEE std.,802.11-2009, published Oct. 29, 2009, or future implementations thereof)802.15-based PANs (e.g., the IEEE 802.15 standard, IEEE std.,802.15-2006, published September 2006, or future implementationsthereof) and 802.16-based WANs (e.g., the IEEE 802.16 standard, IEEEstd., 802.16-2009, published 2009, or future implementations thereof)where MIMO technologies have been adopted and it is desirable toreliably communicate multimedia content. Implementations can also beused for MIMO in 3G networks, 4G networks, cellular networks, WWANs,3GPP networks, LTE networks, LTE-Advanced networks, and Mobile TV, andthe like. Further, while several specific standards have been set forthherein as examples of suitable applications, implementations herein arenot limited to any particular standard or protocol.

At block 204, source coding is carried out by the transmitter, such asto convert the continuous source into a finite stream of bits.

At block 206, distortion minimizing criteria are applied during encodingby the transmitter. By incorporating distortion-minimizing parametersduring the encoding it is possible to mitigate the errors in the bitstream that would otherwise be induced by transmission over the channel.For example, according to some implementations, optimal MIMOmodulation-and-coding schemes, packet sizes and precoding parameters areselected for minimizing distortion that will occur to the source duringencoding, transmission and decoding.

At block 208, the encoded source is transmitted to the receiver over theMIMO channel. Along with the encoded source, the rate-distortioncharacteristics of the source may optionally be transmitted over theMIMO channel so that this information may be used by the receiver towarddistortion-aware link adaptation.

At block 210, the receiver receives the transmission from thetransmitter and decodes the transmission to reconstruct the source.

At block 212, optionally, the receiver can provide feedback to thetransmitter to provide the transmitter with information pertaining tothe distortion-minimizing parameters. The feedback may include thedistortion-minimizing precoding parameters and other link adaptationparameters, such as a MIMO modulation-and-coding scheme and packet size,or may include other information for enabling the transmitter todetermine the distortion minimizing parameters. When the transmitterreceives the feedback, the corresponding distortion minimizingparameters can be applied to the channel encoding.

Source and Channel Coding

As source and channel coding operations are performed at differentcommunication layers, many conventional communication systems implementthe source coding entirely separately from the channel coding. That is,source coding may be performed without taking into account the channelbehavior and channel coding may be performed without considering thenature of the source. In general, multimedia wireless communicationinvolves transmitting analog sources over fading channels whilesatisfying end-to-end distortion and delay specifications of anapplication. For example, delay-limitedness accounts for the presence ofstringent latency and buffer constraints. Accordingly, separation ofsource and channel coding may not be optimal, such as when the channelstate information (CSI) is not available at the transmitters or whenfinite coding blocklengths are used due to practical system limitations.

Some implementations herein adopt a joint source-channel codingtechnique for providing MIMO link adaptation. In the jointsource-channel coding according to implementations herein, the sourcecompression and channel coding are performed jointly, such that theend-to-end distortion for wireless multimedia communication can beminimized by accounting for the impact of both source distortion (e.g.,due to lossy compression) and channel-induced distortion (e.g., due tofading and noise).

MIMO Communications

As mentioned above, multiple-input multiple-output (MIMO) wirelesscommunication uses multiple antennas at both ends of a point-to-pointwireless link. The use of MIMO systems can improve spectral efficiency,link reliability and power efficiency through spatial multiplexing gain,diversity gain and array gain, respectively. Two practical techniquesfor space-time modulation in MIMO systems are transmit diversity andspatial multiplexing. “MIMO diversity” refers to a family of techniques(e.g., space-time coding (STC)) that attempt to spread informationacross transmit antennas to enable robust transmission and substantialreliability and coverage improvement in the presence of fading. “MIMOSpatial multiplexing” (MIMO SM), on the other hand, refers to a form ofspatial modulation that achieves high data rates by dividing theincoming data into multiple substreams and transmitting each substreamon a different antenna, enabling transmission rate growth dependent, atleast in part, upon the number of transmit and receive antennas. Areceiver removes the mixing effect of the channel and demultiplexes thesymbol stream. A MIMO system can benefit from both MIMO diversity andMIMO SM. As a general rule, at low signal-to-noise ratios (SNRs), it ispreferable to use MIMO diversity techniques and at high SNRs it ispreferable to use MIMO SM. Adaptive switching between MIMO diversity andMIMO SM based on the knowledge of the long-term and/or short-termchannel fluctuations at the transmitter enables the highest possiblegains from MIMO techniques in terms of spectral efficiency andreliability. Apart from adaptive switching between MIMO diversity andMIMO spatial multiplexing, MIMO link adaptation techniques herein alsoinclude MIMO precoding and MIMO antenna selection.

Distortion-Aware MIMO Link Adaptation

The inventors herein have determined that there is a tradeoff betweenresolution at the source encoder and robustness at the channel encoder.Accordingly, limiting source distortion and associated quantizationerrors uses a high-rate source code, for which the multiple antennas ofthe MIMO channel are used mainly for multiplexing. Alternatively, thesource can be encoded at a lower rate with more distortion, and then thechannel error probability and associated packet error rate (PER) can bereduced through increased diversity. Consequently, some distortion-awareMIMO link adaptation implementations provided herein take this tradeoffinto consideration toward optimizing end-to-end multimediacommunications over MIMO wireless networks.

Typically, MIMO link adaptation aims to maximize the link throughput,goodput or spectral efficiency, which is achieved when the selected MIMOmodulation and coding scheme (MIMO MCS) transmission mode provides thehighest spectral efficiency based on the channel conditions.Furthermore, packet sizes, i.e., the total number of information bitscarried in a given transmission packet, may also be adapted based on thechannel conditions. With large packet sizes, it may be possible to sendmore information bits over the channel in a given packet transmission,but in such settings more packet errors are likely to be encounteredcompared to transmissions with smaller packet sizes. Consequently, givenchannel state information, it is possible to predict the packet errorrate of all available MIMO MCS modes and packet sizes and choose theMIMO MCS mode and packet size which offers the highest spectralefficiency. Therefore, MIMO link adaptation typically aims to maximizegoodput (also known as throughput) given by the following formula:

goodput={tilde over (R)}*(1−PER)

such that

${( {{MIMO\_ MCS},{P\_ SIZE},Q} ) = {\arg \; {\max\limits_{{{{MIMO}\; \_ \; {MCS}},{P\; \_ \; {SIZE}},Q}\;}{\overset{\sim}{R}*( {1 - {PER}} )}}}},$

where {tilde over (R)} is the space-time transmission rate at thechannel coder determined by the selected MIMO-MCS scheme (including FECtype and code rate, modulation order, MIMO space-time modulationscheme), P_SIZE is the packet size and PER is the packet error rate(PER) determined by the average or long-term receivedsignal-to-interference-and-noise ratio (SINR), instantaneous orstatistical knowledge of the short-term SINR over the MIMO channel,selected MIMO MCS, selected packet size and selected precoding matrix Q.

Instead of attempting to maximize goodput, implementations hereinprovide MIMO link adaptation techniques for minimizing an expected valueof end-to-end distortion by choosing the MIMO MCS, packet size andprecoding matrix Q using the following distortion-based criterion(referred to as distortion-aware MIMO link adaptation):

(MIMO_MCS_(SELECTED) ,P_SIZE_(SELECTED) ,Q _(SELECTED))=arg min_(MIMO)_(_) _(MCS,P) _(_) _(SIZE,Q) D _(ave)(MIMO_MCS,P_SIZE,Q)

where D_(ave)(MIMO_MCS,P_SIZE,Q) represents the average end-to-enddistortion for a given MIMO MCS, packet size and precoding matrix Q. Inother words, the selection of MIMO MCS, packet size, precoding matrix Qand MIMO space-time modulation mode (e.g., MIMO diversity or MIMO SM)for the multimedia transmission is decided according to implementationsherein so that the resulting end-to-end distortionD_(ave)(MIMO_MCS,P_SIZE,Q) is minimized.

For the MIMO diversity mode (e.g., MIMO STC, MIMO OTSBC, etc.) as wellas single-input single-output (SISO) systems, the average end-to-enddistortion at data rate R is given by the formula

D _(ave)(MIMO_MCS_DIV,P_SIZE,Q)=D(b*R)*(1−PER)+D _(max)*PER

For the MIMO SM mode with vertical encoding, where a total of N spatialstreams are sent simultaneously over the MIMO link using a singlespace-time-frequency encoder for all N spatial streams, with eachspatial stream sent at data rate R, the average end-to-end distortion isgiven by the formula

D _(ave)(MIMO_MCS_SM,P_SIZE,Q)=D(N*b*R)*(1−PER)+D _(max)*PER.

In the case of a MIMO vertical encoding architecture with a linearreceiver (e.g., zero-forcing (ZF) or minimum mean square-error (MMSE)receiver) followed by a single space-time-frequency decoder, the packeterror rate (PER) is dictated by the quantity SINR_(min)=min_(n)SINR_(n), such that SINR_(n) is the signal-to-interference-and-noiseratio (SINR) corresponding to the n-th multiplexed MIMO spatial stream(n=1, . . . , N).

For the MIMO SM mode with horizontal encoding, where a total of Nspatial streams are sent simultaneously over the MIMO link using aseparate time-frequency encoder that is associated with each of the Nspatial streams, with each spatial stream sent at data rate R, theaverage end-to-end distortion is given by the formula

${D_{ave}( {{{MIMO\_ MCS}{\_ SM}},{P\_ SIZE},Q} )} = {\sum\limits_{n = 0}^{N}{{D( {n*b*R} )}( {\sum\limits_{K_{n}}^{\;}{\prod\limits_{{k:b_{k}} = 1}^{\;}\; {( {1 - {PER}_{k}} ){\prod\limits_{{l:b_{l}} = 0}^{\;}\; {PER}_{l}}}}} )}}$

where PER_(n) is the packet error rate for the n-th multiplexed MIMOspatial stream (n=1, . . . , N), and for {b_(n)ε{0,1}}_(n=1) ^(N), then

${K_{n} = \{ {{( {b_{1},\ldots \mspace{14mu},b_{N}} )\text{:}\mspace{14mu} {\sum\limits_{k = 1}^{N}b_{k}}} = n} \}},$

after observing that, for the MIMO horizontal encoding architecture,each of the N spatial streams is encoded and decoded independently.

In the above equations, D(b*R) represents a distortion-rate function fora multimedia source, i.e., the distortion that the source incurs afterreconstruction at the decoder (due to quantization errors associatedwith lossy compression by the multimedia codec) as a function of thedata rate R determined by the selected MIMO MCS, D_(max) is the maximumpossible distortion experienced when the source reconstruction at thedecoder is hindered by packet losses and transmission failures, given byD_(max)=D(R=0), and b is a fixed scalar normalization term representingthe ratio between the source code rate and channel code rate to accountfor the rate matching between the multimedia codec and the channelcoder. For example, some implementations assume delay-limited multimediatraffic which cannot be buffered due to tight latency constraints. Thedistortion-rate function D(b*R) is a decreasing function of the datarate R, since a higher source/channel code rate allows for compressionwith lower quantization errors and hence lower distortion. Therate-distortion characteristics may also be dependent on otherapplication and network layer functionalities, such as frame type (e.g.,I-frame, P-frame or B-frame), employed error concealment scheme, networklayer packetization and transmission framework used toward passing thecompressed source from the codec to the channel encoder (e.g., inRTP/UDP), type of layering in the case of advanced source compressionmethods such as scalable video coding (SVC) and application-layerforward error correction FEC (e.g., raptor codes, Reed-Solomon codes,etc.).

In a slow-fading environment, a burst of error would significantlydegrade error performance and thus negatively impact the reliabledecoding of the received signal. If the system can tolerate a certaindelay, retransmitting the signal using Automatic repeat request (ARQ)protocols would help to enhance communication reliability. The mainobjective of an ARQ protocol is to prevent, for each link, the loss offrames due to transmission errors. Frame errors are examined at thereceiving end by an error detection (usually cyclic redundancy check(CRC)) code. If a frame passes the CRC, the receiving end sends anacknowledgement (ACK) of successful transmission to the receiver. If aframe does not pass CRC and the receiver node detects errors in thereceived frame, it sends a negative acknowledgement (NACK), requestingretransmission. The request is repeated until the decoder detects anerror-free transmission. User data and CRC bits may be additionallyprotected by an error correcting code which increases the probability ofsuccessful transmission. In Hybrid ARQ (HARQ) protocols, error detectionand correction are combined in order to obtain better reliability andthroughput. Distortion-aware link adaptation techniques may be designedto accommodate ARQ or HARQ based retransmission mechanisms. Forinstance, in the MIMO diversity mode (e.g., MIMO STC, MIMO OTSBC, etc.)as well as single-input single-output (SISO) systems, the averageend-to-end distortion at data rate R is given by formula (1), can berevised as follows:

D _(ave)(MIMO_MCS_DIV,P_SIZE,Q)=D(bR)*(1−PER₁)+D(bR/2)*PER₁*(1−PER₂)+ .. . D(bR/M)*(Π_(j=1) ^(M-1)PER_(j)(1−PER_(M))+D _(max)Π_(j=1)^(M)PER_(j))

where PER_(j) is the probability of packet decoding error after the j-thtransmission, and M is the maximum number of allowed transmissions.

Selection of the MIMO modulation and coding scheme (MIMO-MCS) hereinincludes (a) selection of the modulation order, (b) selection of theforward error correction (FEC) type and coding rate, and (c)determination of which space-time modulation techniques will be used.Options for space-time modulation include spatial multiplexing (SM),space-time coding (STC), orthogonal space-time block coding (OSTBC),beamforming, etc., including metrics such as STC rate, rank, number ofMIMO streams, and the like. Special cases for multi-antennacommunications, such as single-input-multiple-output (SIMO) modes canalso be used among space-time modulation options. Further, the MIMO linkadaptation herein may be employed in conjunction with anydistortion-rate function, and implementations may include any method forincorporating distortion criteria in the appropriate selection of a MIMOMCS.

Furthermore, the selection of the precoding matrix Q herein includes (a)beamforming to convert a MIMO channel into an equivalent single-inputsingle-output (SISO) channel, (b) precoded spatial multiplexing, (c)precoded orthogonal space-time block coding (OSTBC), (d) transmit powerallocation and covariance optimization, and (e) transmit antennaselection techniques where M out of M_(t) transmit antennas are selectedfor transmission.

Implementations of the distortion-aware MIMO link adaptation criterionallow for realizing the benefits of joint source-channel coding byadapting the source and channel coding rate to minimize end-to-enddistortion, rather than maximize throughput or spectral efficiency. Itshould be noted that in order to use this distortion-based MIMO linkadaptation criterion, only the distortion-rate function D(b*R) may beavailable at the radio level (which is determined by the nature of themultimedia source as well as the compression capabilities of the codecor source encoder), so this information can be passed from theapplication layer to the PHY/MAC (physical/media access control) layer.It should be further noted that the above MIMO link adaptation criterionmay be employed in conjunction with any distortion-rate function or anyfunction of rate that quantifies the user's quality of experience forthe multimedia application, and that implementations herein also includeany method for incorporating distortion criteria or any other criteriathat determine multimedia quality in the appropriate selection of a MIMOMCS, packet size and precoding matrix.

Open Loop and Closed Loop MIMO Link Adaptation

Implementations of the distortion-aware MIMO link adaptation frameworkare applicable for both open-loop and closed-loop MIMO systems. Thesystem architectures for the transmitter and receiver components aredepicted in FIG. 3 for an open-loop MIMO communication configuration,and in FIG. 4 for a closed-loop MIMO communication configuration havinglimited rate feedback of link adaptation parameters. The open-loop MIMOsetup may be more relevant for scenarios in which reliable estimationand feedback of dynamic channel variations and link adaptationparameters is generally difficult (e.g., as in high mobility scenarios),so the distortion-aware MIMO link adaptation can be performed at thetransmitter based on the knowledge of the long-term channel variationsand statistics of the instantaneous or short-term channel variations. Onthe other hand, the closed-loop MIMO setup may be more relevant forsituations where the channel variations occur over a slower time scale(e.g., as in low-mobility scenarios) to allow for reliable channelestimation and feedback of link adaptation parameters from the receiverto the transmitter (i.e., using mechanisms such as a channel qualityindicator (CQI) feedback mechanism). The closed-loop MIMO configurationmay also use statistical or long-term channel knowledge in communicationscenarios where link adaptation parameters are determined by thereceiver and fed back to the transmitter, for instance, as in the uplinkof cellular communications, where it is difficult to obtain reliableestimates of the instantaneous or short-term channel conditions forvarious reasons such as high mobility or high user density.

Open-Loop Architecture

FIG. 3 illustrates a block diagram of an example of an open-loopdistortion-aware MIMO link adaptation architecture 300 according to someimplementations herein, in which link adaptation parameters aredetermined and applied at the transmitter. In the architecture of FIG.3, a transmitter 302 is able to communicate with a receiver 304 via aMIMO channel H 306. In the illustrated implementation, transmitter 302includes a source encoder, shown as source coding block 308, and achannel encoder, shown as channel coding block 310. The source codingblock 308 is configured to compress or otherwise encode a source 312,such as a multimedia source, to create source-encoded data 314, and passthe source-encoded data 314 along with rate-distortion information 316of the source-encoded data 314 to the channel coding block 310. Forexample, in the case that source 312 is a video received by sourcecoding block 308 as a stream of video information, source coding block308 encodes (e.g., compresses) the received stream of video informationinto a format suitable for transmission (one non-limiting example of asuitable format is the H.264/MPEG-4 AVC video coding standard developedby the ITU-T Video Coding Experts Group (VCEG) together with the ISO/IECMoving Picture Experts Group (MPEG), finalized May, 2003, or the like).Furthermore, in the case in which the source 312 is an analog stream,source coding block 308 further converts the analog stream into adigital form during encoding. Finally, the source coding block 308 maybe configured to compress or otherwise encode a source 312 and createsource-encoded data 314 with a selected source coding rate that matchesthe MCS determined by the channel coding block 310 subject to thedistortion-aware criteria for MIMO MCS selection, as discussed herein.

Consequently, the encoding carried out by the source coding block 308 isat least partially dependent upon the nature of the multimedia source aswell as the compression capabilities of the codec or source encoder.Further, according to implementations herein, rate distortioninformation 316 is determined for the encoded source and thisinformation is also passed to the channel coding block 310 to taken intoaccount for distortion awareness during the channel encoding of thesource-encoded data 314. For example, rate distortion characteristicsfor various codecs and source encoding of various different media typescan be determined and/or observed, and passed to the channel codingblock 310 by the source coding block 308 based upon the type of sourcecoding used by source coding block 308. The rate-distortioncharacteristics of the source utilized at the channel coding block 310for link adaptation purposes may also be dependent on other applicationand network layer functionalities, such as frame type (e.g., I-frame,P-frame or B-frame), network layer packetization and transmissionframework used toward passing the compressed source from the codec tothe channel encoder (e.g., in RTP/UDP), type of layering in the case ofadvanced source compression methods such as scalable video coding (SVC)and application-layer forward error correction FEC (e.g., raptor codes,Reed-Solomon codes, etc.).

The channel coding block 310 includes a distortion-aware time-frequencyforward error correction (FEC) outer coding and interleaving block 318,followed by a distortion-aware MIMO space-time (ST) modulation block320, which is then followed by a distortion-aware MIMO precoding blockor component 322 to produce channel-encoded data 324. According to someimplementations, precoding is carried out according to a precodingframework for optimal design of the precoding matrix Q, as describedadditionally below. Following precoding, the channel-encoded data istransmitted by multi-antenna transmission to the receiver 304 over MIMOchannel 306. The MIMO space-time modulation block 320 can either operatein the MIMO diversity mode, as distortion-aware MIMO STC block 328, orin the MIMO spatial multiplexing mode as distortion-aware MIMO SM block330. In the MIMO diversity mode, output bits of the FEC coding andinterleaving block 318 are first modulated by symbol mapping in a symbolmodulation block 332 at high quadrature amplitude modulation (QAM), andthen re-encoded using a space-time code (STC) into multiple spatialstreams at space-time coding block 334. Alternatively, in the MIMOspatial multiplexing mode, the coded/interleaved bits output from theFEC coding and interleaving block 318 are de-multiplexed into multiplespatial streams by a DEMUX block 336, and each stream is then modulatedby symbol mapping in a plurality of symbol modulation blocks 338 at lowQAM. The decision on whether to use the distortion-aware MIMO STC block328, or the distortion-aware MIMO SM block 330 is dependent upon thedetermined distortion-aware criteria for MIMO MCS selection, asdiscussed herein.

At the receiver end, a space-time decoder block 340 in receiver 304 isconfigured to recover the transmitted source data from a noisy corruptedreceived version transmitted over the MIMO wireless channel, followingthe multi-antenna reception. The recovered data stream is passed to asource decoding block 342, which reconstructs the source with the goalof minimizing the distortion between the original source and areconstructed source 344. For example, in the case of a multimediasource, such as an audiovisual multimedia content item (e.g.,television, movie, video, or the like), the goal is to minimizedistortions introduced by the encoding and decoding of the content itemand the transmission of the content item over a noisy transmissionchannel.

The source-encoded data 314 received from the source coding block 308 ispassed through the channel encoding blocks 318, 320, 322 beforemulti-antenna transmission. According to implementations herein, all ofthese radio-level channel encoder blocks 318, 320, 322 have the propertyof “distortion-awareness” since these radio-level channel encoder blocks318, 320, 322 are configured to execute implementations of thedistortion-aware MIMO link adaptation strategy for selected MIMO MCSscheme, packet size and precoding set forth herein. Based upon formulasdiscussed below link adaptation parameters 346, i.e., MIMO MCS (such asFEC code rate and MIMO space-time modulation scheme), packet size andprecoding matrix Q are determined and provided to the radio-levelchannel encoder blocks 318, 320, 322 for implementing the distortionawareness of these blocks. Examples of determining the link adaptationparameters 346 for the open-loop implementations are described furtherbelow.

Closed-Loop Architecture

FIG. 4 illustrates a block diagram of an example of a closed-loopdistortion-aware MIMO link adaptation architecture 400 according to someimplementations herein, in which link adaptation parameters aredetermined at the receiver and are fed back for application at thetransmitter. In the architecture of FIG. 4, similar to that of FIG. 3described above, a transmitter 402 is able to communicate with areceiver 404 via a MIMO channel 406. In the illustrated implementation,transmitter 402 includes a source encoder, shown as source coding block408, and a channel encoder, shown as distortion-aware channel codingblock 410. The source coding block 408 is configured to compress andotherwise encode a source 412, such as a multimedia source, and pass thesource-encoded data 414 along with, in some implementations,rate-distortion information 416 of the source-encoded data 414 to thedistortion-aware channel coding block 410. However, in otherimplementations, the source coding block 408 may not pass ratedistortion information 416 to the channel coding block 410. Instead, asdiscussed further below, the rate distortion information may bedetermined directly by the receiver 404 and taken into considerationwhen preparing feedback that is provided to the distortion-aware channelcoding block 410. Hence passing the rate distortion information 416 fromthe source coding block 408 is used in some implementations of theclosed loop architecture, and is labeled as Option A (Op. A) in FIG. 4.Alternatively, or in addition, the rate distortion information may bedetermined independently at the receiver 404, which is labeled as OptionB (Op. B) in FIG. 4, and which is discussed additionally below.

The channel coding block 410 includes a time-frequency forward errorcorrection (FEC) outer coding and interleaving block 418, followed by aMIMO space-time (ST) modulation block 420, which is then followed by aMIMO precoding block or component 422. The MIMO precoding block 422 mayexecute precoding according to a framework for optimal design of theprecoding matrix Q, as described additionally below, to producechannel-encoded data 424. The channel encoded data 424 is sent toreceiver 404 over MIMO channel 406 (along with rate-distortioninformation 416 in the case of Op. A). Similar to the configurationdiscussed above with reference to FIG. 3, the MIMO ST modulation block420 can either operate in the MIMO diversity mode as MIMO STC block 428,or in the MIMO spatial multiplexing mode as MIMO SM block 430. In theMIMO diversity mode, output bits of the channel coding and interleavingblock 418 are first modulated by symbol mapping in a symbol modulationblock 432 at high QAM, and then re-encoded using a space-time code (STC)into multiple spatial streams at space-time coding block 434.Alternatively, in the MIMO spatial multiplexing mode, thecoded/interleaved bits output from the coding and interleaving block 418are de-multiplexed into multiple spatial streams by a DEMUX block 436and each stream is then modulated by symbol mapping in a plurality ofsymbol modulation blocks 438 at low QAM. The decision on whether to usethe MIMO STC block 428, or the MIMO SM block 430 is dependent upon thedetermined distortion-aware criteria for MIMO MCS selection, which isprovided to the distortion-aware channel coding block 410 by feedbackfrom the receiver 404.

At the receiver 404, a space-time decoding block 440 is configured torecover the transmitted source data from a noisy corrupted receivedversion transmitted over the MIMO wireless channel, following themulti-antenna reception. The recovered data stream is passed to a sourcedecoding block 442, which reconstructs the source with the goal ofminimizing the distortion between the original source 412 and areconstructed source 444.

For the closed-loop distortion-aware MIMO link adaptation architecture400 illustrated in FIG. 4, the receiver 404 also includes adistortion-aware feedback block 446 that periodically provides feedbackto transmitter 402 for enabling the distortion awareness of thedistortion-aware channel coding block 410. For example, thedistortion-aware feedback block 446 at the receiver 404 may determinefrom the space-time decoding block 440 link adaptation information 448(i.e., the estimated MIMO channel parameters, and the MIMO MCS, packetsize and precoding matrix Q parameters). The distortion-aware feedbackblock 446 uses the link adaptation information 448 along with ratedistortion information 416 (Op. A) and/or rate distortion information450 (Op. B) to determine distortion-minimizing link adaptationparameters 452, e.g., a MIMO MCS scheme, packet size and precodingmatrix Q. After the distortion-minimizing MIMO link adaptationparameters 452 have been determined at the receiver 404 based onreceiver's knowledge of the long-term channel variations along with theinstantaneous or statistical knowledge of short-term MIMO channelrealizations, the link adaptation parameters 452 are fed back to thetransmitter 402.

In addition, according to some implementations, as discussed above, whendetermining distortion-minimizing MIMO link adaptation parameters 452,the distortion-aware feedback block 446 may also gather therate-distortion information 450 about the multimedia source from thesource decoding block 442 (Op. B). Alternatively, or in addition,transmitter 402 may send rate-distortion information 416 on the sourcealong with channel-encoded data 424 to receiver 404 over the MIMOchannel 406 (Op. A), so that distortion-aware feedback block 446 atreceiver 404 may utilize this information in determiningdistortion-minimizing MIMO link adaptation parameters 452. The ratedistortion information 416 and/or 450 are taken into consideration bydistortion-aware feedback block 446 when determining the distortionminimizing link adaptation parameters 452, e.g., MIMO MCS, packet sizeand precoding matrix, which are then passed to the transmitter 402through a feedback channel. For example, transmitter 402 may beincorporated into a first device that also includes a receiver (notshown), while receiver 404 may be incorporated into a second device thatalso includes a transmitter (not shown), thus enabling the receiver 404to provide feedback wirelessly to the transmitter 402 such as over MIMOchannel 406, or other wireless channel, link, or the like.

Distortion-Aware Precoding Framework

Implementations herein provide a distortion-aware precoding frameworkfor optimal design of the precoding matrix Q to enable distortion-awarelink adaptation. As mentioned above, precoding is a processing techniquethat exploits channel state information (CSI) by operating on the signalbefore the signal is transmitted. For example, a precoding componentaccording to some implementations herein may essentially function as amultimode beamformer able to optimally match the input signal on oneside to the channel on the other side. In some implementations, theprecoding component may do this by splitting the transmit signal intoorthogonal spatial eigenbeams with per-beam power allocation in whichhigher power is assigned along the beams where the channel is strong,but lower or no power is assigned along the beams where the channel isweak. Further, the precoding parameters may vary based on the type ofstatistical channel state information (CSI) available and performancecriteria.

As an example, consider the transmitter to be a base station able tocommunicate with a plurality of mobile stations as receivers. In orderto optimally perform radio resource management and link adaptation, thetransmitter may learn the link qualities to each receiver, i.e., towardexecuting functions such as scheduling and MCS selection. To this end,channel quality indicator (CQI) feedback mechanisms may be designed, sothat each receiver can periodically report its channel state informationto the transmitter. Relevant CQI metrics in this context includephysical signal-to-interference-and-noise ratio (SINR) (also known ascarrier-to-interference-and-noise ratio (CINR)) and effective SINR(E-SINR or E-CINR), channel state information (e.g., statistical channelinformation, such as channel mean or channel covariance,transmit/receive correlation matrices, etc., or instantaneous channelinformation such as channel Demmel condition number), as well asrecommendations of the receiver for a number of link adaptation modessuch as MCS selection, MIMO space-time modulation mode, MIMO STC rate,packet size index and precoding matrix index (PMI). To enable a CQIfeedback mechanism in the distortion-aware MIMO link adaptation systemarchitecture, the space-time decoder at the receiving end also includesa distortion-aware feedback block 446, as illustrated in FIG. 4, whichdetermines the link adaptation information (i.e., SINR information,statistical or instantaneous channel state information, MIMO MCS, packetsize and precoding matrix) to be periodically fed back to thetransmitter after the distortion-minimizing MIMO link adaptationparameters have been determined at the receiver based on receiver'sknowledge of the average or long-term received SINR and instantaneous orstatistical knowledge of the short-term SINR over the MIMO channel. Tobe used as part of this process, the distortion-aware feedback designblock may gather the rate-distortion information about the multimediasource from the source decoding block 442.

In a point-to-point single-user MIMO communication system with M_(t)transmit and M_(r) receive antennas over a coding blocklength T, theM_(r)×T received signal is given by

Y=HQS+N,

where H is the M_(r)×M_(t) complex random channel matrix representingthe MIMO link (which remains fixed over the entire coding blocklengthT), S is the M×T transmitted space-time block codeword, Q is the M_(t)×Mlinear precoding matrix (M≦M_(t) is a precoding design parameter) suchthat trace(Q*Q′)=1, where Q′ is the Hermitian of Q, and N represents theM_(r)×T additive white Gaussian noise (AWGN) noise matrix where eachentry has zero mean and variance σ². The average receivedsignal-to-noise ratio (SNR) for the MIMO link is given by γ=P/σ², withthe average transmit power constraint (1/T)*E└∥S∥_(F) ²┘≦P, where∥A∥_(F) is the Frobenius norm of matrix A and E represents theexpectation operation. The receiver has a priori the precoding matrix Qand may treat the combination HQ as an effective channel. Consequently,the normalized M_(t)×M_(t) transmit covariance matrix for the M_(t)×Ttransmitted signal (after precoding) is given by

Φ=Q*Ω*Q′,  (1)

where the normalized codeword covariance matrix

$\Omega = {\frac{1}{TP}{E\lbrack {SS}^{\prime} \rbrack}}$

depends on the choice of the space-time encoding method (e.g.,space-time coding, spatial multiplexing, etc.). Based on equation (1),the normalized M_(r)×M_(r) receive covariance matrix Ψ is then given by

Ψ=γHQΩQ′H′+I=γHΦH′+I,  (2)

where I is the M_(r)×M_(r) identity matrix.

According to some implementations, an objective for precoding is toensure that the transmit covariance matrix Φ has a desirable structureto optimize certain performance metrics (e.g., capacity, pair-wise errorprobability, symbol error rate, etc.). In that context, a linearprecoding matrix Q functions as a multimode beamformer for optimallymatching the input signal S on one side to the channel H on the otherside. The precoding component does this by splitting the transmit signalinto orthogonal spatial eigen-beams with per-beam power allocation suchthat the precoding component assigns higher power along the beams wherethe channel is strong but lower or no power along the weak. Essentially,a precoding component has two effects: (1) decoupling the input signalinto orthogonal spatial modes, such as in the form of eigen-beams, and(2) allocating power over these beams, such as based on the CSI (channelstate information). If the precoded, orthogonal spatial-beams match thechannel eigen-directions (the eigenvectors of H′H), there will be nointerference among signals sent on different modes, thus creatingparallel channels and allowing transmission of independent signalstreams. However, this effect uses full channel knowledge available atthe transmitter. With partial CSI and statistical channel knowledge, theprecoding component may approximately match the eigen-beams to thechannel eigen-directions, and therefore is able to reduce theinterference among signals sent on these beams. This is a decouplingeffect enabled by precoding.

Moreover, the precoding component may allocate power on the beams. Forexample, for orthogonal eigen-beams, when all the beams have equalpower, the total radiation pattern of the transmit antenna array isisotropic. Thus, through power allocation, the precoding component mayeffectively create a radiation shape to match to the channel based onthe CSI. Consequently, higher power is sent in the directions where thechannel is strong and reduced or no power is sent in the directionswhere the channel is weak. Accordingly, precoding according to someimplementations herein may include two parts, namely, (1) beamformingdirections and (2) power allocation across the beams.

Further, implementations herein provide a specific framework for theoptimal design of the precoding matrix Q in order to enabledistortion-aware link adaptation. One goal of the framework is to ensurethat the optimal precoding matrix Q is selected such that the resultingreceive covariance matrix Ψ given in equation (2) minimizes averageend-to-end distortion. More specifically, considering the eigen-valuedecomposition of the transmit covariance matrix Φ as Φ=U_(Φ)Λ_(Φ)U′_(Φ),implementations may determine an optimal precoding design yielding adistortion-minimizing parameter set for the eigen-vectors U_(Φ) (i.e., aunitary matrix representing the beamforming matrix for the orthogonaltransmit beam directions) and eigen-values Λ_(Φ) (i.e., a diagonalmatrix representing power allocation across these beams) for the receivecovariance matrix, re-expressed as

Ψ=γHU _(Φ)Λ_(Φ) U′ _(Φ) H′+I.  (3)

Implementations are able to address distortion-aware precodingtechniques for long-term link adaptation, which is suitable insituations for which reliable feedback of dynamic channel variations andlink adaptation parameters is generally difficult (e.g., as in highmobility or high user density scenarios). In such a setting, thedistortion-aware MIMO link adaptation may be performed based on theknowledge of the long-term channel variations and statistics of theinstantaneous or short-term channel variations. As part of the long-termlink adaptation, availability of various types of statistical channelknowledge may be considered according to two different example CSImodels, as discussed below.

Example CSI Model 1 Known Channel Mean

According to a first example CSI model, the CSI includes a known channelmean. In this example, the estimates for the channel coefficients areavailable subject to an estimation error. In other words, the long-termlink adaptation can be performed subject to the availability of theM_(r)×M_(t) channel mean matrix H=E[H] (representing the channelestimates) such that H=H+{tilde over (H)}, where {tilde over (H)}contains uncorrelated independent and identically distributed (i.i.d.)circularly-symmetric complex Gaussian entries (i.e., the covariance isthe identity matrix) representing the estimation error. In this setting,the long-term link adaptation can be performed based on the followingrule

(MIMO_MCS,P_SIZE,Q)=ƒ(SINR,{tilde over (H)}),

where the distortion-aware MIMO link adaptation function ƒ maps theaverage or long-term SINR and channel mean H to a MIMO-MCS scheme,packet size P_SIZE and a precoding matrix Q.

Example CSI Model 2 Known Channel Transmit and Receive CorrelationStructure

According to a second example CSI model, the CSI includes a knownchannel transmit and receive correlation structure. In this example, thechannel is assumed to be varying too fast and therefore its dynamicvariations cannot be estimated, but the correlation structure of thechannel can be tracked. In other words, the link adaptation can beperformed subject to the availability of transmit and receivecorrelation matrices of the MIMO channel W_(T) and W_(R), of dimensionsM_(t)×M_(t) and M_(r)×M_(r), respectively, such that the MIMO channelmatrix H can be represented as H=W_(R) ^(1/2)H_(w)W_(T) ^(1/2), wherethe M_(r)×M_(t) complex random channel matrix H_(w) contains theuncorrelated i.i.d. channel coefficients. In this setting, the long-termlink adaptation can be performed subject to the following rule

(MIMO_MCS,P_SIZE,Q)=ƒ(SINR,W _(T) ,W _(R)),

where the distortion-aware MIMO link adaptation function ƒ maps theaverage or long-term SINR and channel correlation matrices W_(T) andW_(R) to a MIMO-MCS scheme, packet size and a precoding matrix Q. Ingeneral, the long-term link adaptation may account for all of channelmean H and correlation matrices W_(T) and W_(R) to determine theMIMO-MCS scheme, packet size and precoding matrix Q and thereforecorresponds to the mapping

(MIMO_MCS,P_SIZE,Q)=ƒ(SINR, H,W _(T) ,W _(R)).

Design Criteria for Distortion-Aware Precoding

Subject to availability of statistical channel knowledge specified bythe various statistical CSI models above (and/or other CSI models basedon statistical channel knowledge), a general criterion for selecting anoptimal precoding matrix that minimizes end-to-end distortion may begiven by

$\begin{matrix}{{( {{MIMO\_ MCS}_{SELECTED},{P\_ SIZE}_{SELECTED},Q_{SELECTED}}\underset{\_}{)}  = {\min\limits_{R,{P\; \_ \; {SIZE}},Q}{D_{ave}( {R,{P\_ SIZE},Q,{SINR},\overset{\_}{H},W_{T},W_{R}} )}}},\mspace{20mu} {{{subject}\mspace{14mu} {to}\mspace{14mu} {{trace}( {Q*Q^{\prime}} )}} = 1}} & (4) \\{\mspace{79mu} {with}} & \; \\{D_{ave} = {E_{H}\lbrack {{{D( {b*R} )}*( {1 - {{PER}( {R,{P\_ SIZE},Q,{SINR},\overset{\_}{H},W_{T},W_{R}} )}} )} + {D_{{ma}\; x}*{{PER}( {R,{P\_ SIZE},Q,{SINR},\overset{\_}{H},W_{T},W_{R}} )}}} \rbrack}} & (5)\end{matrix}$

where R is the space-time transmission rate at the channel coderdetermined by the selected MIMO-MCS scheme (e.g., including FEC type andcode rate, modulation order, MIMO space-time modulation scheme) and PERis the packet error rate (PER) determined by the average or long termreceived signal-to-interference and noise ratio (SINR), statisticalknowledge of the short-term SINR over the MIMO channel represented bychannel mean H and correlation matrices W_(T) and W_(R), data rate forthe selected MIMO MCS, selected packet size and selected precodingmatrix Q. By determining average end-to-end distortion, D_(ave), for theparameters above, the precoding can be used to minimize the end-to-enddistortion.

Distortion-Aware Precoding Scheme for Example CSI Model 1

Returning to the example CSI model 1 discussed above, selection criteriafor distortion-aware precoding design for this example CSI model is nowdescribed. This enables determination of optimal precoding designsyielding the distortion-minimizing parameter set for the eigen-vectorsU_(Φ) (i.e., for transmit beam directions), and eigen-values Λ_(Φ)(i.e., for power allocation across beams). In this example, thesingular-value decomposition of the channel mean H may be H=U _(H) Σ_(H) V′ _(H) . Then the optimal precoding beam directions to minimizeend-to-end distortion would be obtained from the right singular vectorsof H, and are given by U_(Φ)=V _(H) (these directions are also theeigen-vectors of H′H) such that the channel mean eigen-directions becomethe statistically preferred directions. Consequently, the optimalreceive covariance matrix in equation (3) can be expressed for this caseas

Ψ_(CSIT-1)=γ(Σ _(H) +{tilde over (H)})Λ_(Φ)(Σ _(H) +{tilde over(H)})′+I,trace(Λ_(Φ))=1.

The remaining task is to select the set of eigen-values Λ_(Φ) by usingthe general distortion-minimizing criterion of equation (4). Morespecifically, the selection criterion in equation (4) for this case canbe expressed as

$\begin{matrix}{{( {{MIMO\_ MCS}^{opt},{P\_ SIZE}^{opt},\Lambda_{\Phi}^{opt}} ) = {\min\limits_{R,{P\; \_ \; {SIZE}},\Lambda_{\Phi}}{D_{ave}( {R,{P\_ SIZE},\Lambda_{\Phi},{SINR},\Psi_{{CSIT} - 1}} )}}}\mspace{20mu} {{{subject}\mspace{14mu} {to}\mspace{14mu} {{trace}( \Lambda_{\Phi} )}} = 1}} & (6) \\{\mspace{79mu} {with}} & \; \\{D_{ave} = {E_{H}\lbrack {{{D( {b*R} )}*( {1 - {{PER}( {R,{P\_ SIZE},\Lambda_{\Phi},{SINR},\Psi_{{CSIT} - 1}} )}} )} + {D_{{ma}\; x}*{{PER}( {R,{P\_ SIZE},\Lambda_{\Phi},{SINR},\Psi_{{CSIT} - 1}} )}}} \rbrack}} & (7)\end{matrix}$

Additionally, while the selection of the beamforming matrix U_(Φ) forthe distortion-aware precoding scheme for example CSI model 1 may beidentical to design optimizations with respect to many other performancemetrics (e.g., capacity, pair-wise error probability, etc.), thedistortion-minimizing power allocation given by the set of eigen valuesΛ_(Φ) may be quite different from the classical approaches.

Distortion-Aware Precoding Scheme for Example CSI Model 2

Returning to the example CSI model 2 discussed above, the eigen-valuedecompositions of the transmit correlation matrix W_(T) may beW_(T)=U_(T)Λ_(T)U′_(T) and for the receive correlation matrix W_(R) maybe W_(R)=U_(R)Λ_(R)U′_(R). Then the optimal precoding beam directions tominimize end-to-end distortion would be obtained from the eigen-vectorsof the transmit correlation matrix, i.e., eigen-vectors of W_(T), andwhich are given by U_(Φ)=U_(T) such that the correlation among thetransmit antennas may dictate the statistically preferred beamdirections. Consequently, an optimal receive covariance matrix inequation (3) can be expressed for this case as

Ψ_(CSIT-2)=γ(Λ_(R) ^(1/2) H _(w)Λ_(T) ^(1/2))Λ_(Φ)(Λ_(T) ^(1/2) H′_(w)Λ_(R) ^(1/2))+I,trace(Λ_(Φ))=1.

This expression can be further simplified as

${\Psi_{{CSIT} - 2} = {{\gamma {\sum\limits_{m = 1}^{M_{t}}{\lambda_{m}^{(T)}\lambda_{m}^{(\Phi)}{\overset{arrow}{h}}_{m}{\overset{arrow}{h}}_{m}^{\prime}}}} + I}},{{\sum\limits_{m = 1}^{M}\lambda_{m}^{(\Phi)}} = 1}$

where λ_(m) ^((T)) is m-th diagonal entry of Λ_(T) (i.e., m-theigenvalue of W_(T)), λ_(m) ^((Φ)) is the m-th diagonal entry of Λ_(Φ),and {right arrow over (h)}_(m) is the M_(r)×1 column vector given by(m=1, . . . , M_(t)) such that

${\overset{arrow}{h}}_{m} = \begin{bmatrix}{\sqrt{\lambda_{1}^{(R)}}d_{1,m}} \\{\sqrt{\lambda_{2}^{(R)}}d_{2,m}} \\\vdots \\\vdots \\{\sqrt{\lambda_{M_{r}}^{(R)}}d_{M_{r},m}}\end{bmatrix}$

where λ_(n) ^((R)) is the n-th diagonal entry of Λ_(R) (i.e., n-theigenvalue of W_(R)), and d_(n,m) is the entry in the n-th row and m-thcolumn of matrix H_(w) (n=1, . . . , M_(r)).

The remaining task is to select a set of eigen-values Λ_(Φ) by using thegeneral distortion-minimizing criterion of equation (4). Morespecifically, the selection criterion in equation (4) may be expressedfor this case as

$\begin{matrix}{{( {{MIMO\_ MCS}^{opt},{P\_ SIZE}^{opt},\Lambda_{\Phi}^{opt}} ) = {\min\limits_{R,{P\; \_ \; {SIZE}},\Lambda_{\Phi}}{D_{ave}( {R,{P\_ SIZE},\Lambda_{\Phi},{SINR},\Psi_{{CSIT} - 2}} )}}}\mspace{20mu} {{{subject}\mspace{14mu} {to}\mspace{14mu} {{trace}( \Lambda_{\Phi} )}} = 1}} & (8) \\{\mspace{79mu} {with}} & \; \\{D_{ave} = {E_{H}\lbrack {{{D( {b*R} )}*( {1 - {{PER}( {R,{P\_ SIZE},\Lambda_{\Phi},{SINR},\Psi_{{CSIT} - 2}} )}} )} + {D_{{ma}\; x}*{{PER}( {R,{P\_ SIZE},\Lambda_{\Phi},{SINR},\Psi_{{CSIT} - 2}} )}}} \rbrack}} & (9)\end{matrix}$

Further, while the selection of the beamforming matrix U_(Φ) for thedistortion-aware precoding scheme for example CSI model 2 may beidentical to design optimizations with respect to many other performancemetrics (e.g., capacity, pair-wise error probability, etc.), thedistortion-minimizing power allocation given by the set of eigen valuesΛ_(Φ) may be quite different from these classical approaches. Moreover,while the optimal beam directions for distortion-aware precoding may beindependent of the receive correlation matrix, and determined by thetransmit correlation matrix, the power allocation across these beams canbe a function of both transmit and receive antenna correlation matrices.

Example Open-Loop Process

FIG. 5 illustrates a flow diagram of an example open loop process 500for distortion-aware link adaptation according to some implementationsherein. In the flow diagram, the operations are summarized in individualblocks. The operations may be performed in hardware, or asprocessor-executable instructions (software or firmware) that may beexecuted by a processor. Further, the process 500 may, but need notnecessarily, be implemented using the system of FIG. 3. Consequently, byway of explanation, and not limitation, the process 500 is described inthe context of the system of FIG. 3.

At block 502, a source for transmission is provided to a transmitter.For example, as discussed above, the source may be a multimediacommunication, including voice over IP, speech and audio communications,mobile TV, mobile video services, or the like.

At block 504, source encoding of the source is performed by a sourceencoder of the transmitter. For example, as discussed above, the sourcemay be compressed or otherwise encoded in preparation for transmission.

At block 506, the source-encoded data and corresponding rate distortioninformation are provided to the channel encoder of the transmitter. Forexample, the rate distortion information may be based upon the type ofsource encoding performed on the source, and may include knownstatistical or observed data.

At block 508, the link adaptation parameters (e.g., the MIMO MCS scheme,packet size and precoding matrix) are determined for thedistortion-aware channel encoder. For example, link adaptationparameters may include FEC code rate and MIMO space-time modulation forthe MIMO MCS scheme, packet size and the precoding matrix Q determinedbased upon the received rate distortion information and statisticalinformation stored at the transmitter for access by the channel encoder.Further, a lookup table or other stored information based upon knownstatistics (such as SNR values and distortion vectors) of the channelbeing used may be provided for determining optimal link adaptationparameters for minimizing end-to-end distortion. FIG. 7 and thecorresponding description set forth below provide an example ofdetermining the distortion minimizing link adaptation MCS, packet sizeand precoding parameters according to some implementations herein.

At block 510, channel encoding of the source-encoded data is performedbased on the selected link adaptation parameters to producechannel-encoded data.

At block 512, the channel-encoded data is transmitted to the receiverover the MIMO channel.

At block 514, the transmitted encoded data is reconstructed at thereceiver to provide a reconstructed source with minimized distortion.

Example Closed-Loop Process

FIG. 6 illustrates a flow diagram of an example closed-loop process 600for distortion-aware link adaptation according to some implementationsherein. In the flow diagram, the operations are summarized in individualblocks. The operations may be performed in hardware, or asprocessor-executable instructions (software or firmware) that may beexecuted by a processor. Further, the process 600 may, but need notnecessarily, be implemented using the system of FIG. 4. Consequently, byway of explanation, and not limitation, the process 600 is described inthe context of the system of FIG. 4.

At block 602, a source for transmission is provided to the transmitterof the wireless communications device.

At block 604, source encoding of the source data is performed by asource encoder of the transmitter. For example, as discussed above, thesource data may be compressed or otherwise encoded in preparation fortransmission.

At block 606, the encoded source data (and corresponding rate distortioninformation in the case of Op. A, as described in FIG. 4) are providedto the channel encoder of the transmitter.

At block 608, the link adaptation parameters are received by thedistortion-aware channel encoder. For example, link adaptationparameters may include a selected MIMO MCS scheme, packet size andprecoding matrix determined by the receiver and provided as feedback.FIG. 7 and the corresponding description set forth below provide anexample of how the receiver determines the distortion minimizing linkadaptation parameters for closed-loop distortion-aware link adaptationaccording to some implementations herein. For example, based on receivedcommunications, the receiver can possess reliable and accurate knowledgeon the instantaneous or short-term MIMO channel realizations, whichholds for typical communication scenarios since the wireless channelvariations are slow enough to allow for reliable channel estimation andfeedback of link adaptation parameters (e.g., as in low mobilityscenarios). Alternatively, the distortion-aware MIMO link adaptation atthe receiver may also be based on the knowledge of the long-term channelvariations and the statistical knowledge of the instantaneous orshort-term MIMO channel realizations. In that case, FIG. 7 and thecorresponding description set forth below may provide another example ofhow the receiver may determine the distortion minimizing link adaptationparameters for closed-loop distortion-aware link adaptation according tosome implementations herein, such as when the receiver cannot reliablyestimate the dynamic variations of the MIMO channel (e.g., as in highmobility scenarios), and therefore the distortion-aware MIMO linkadaptation at the receiver would be based on the knowledge of thelong-term channel variations and the statistical knowledge of theinstantaneous or short-term MIMO channel realizations. Thus, in thesealternative implementations, the receiver can determine the linkadaptation parameters according to the description of FIG. 6 and providethe determined link adaptation parameters as feedback to thetransmitter.

At block 610, channel encoding of the source-encoded data is performedbased on the MIMO-MCS method and packet size selected and the precodingmatrix received from the receiver to produce channel-encoded data.Furthermore, if feedback has not yet been received, or if feedback isnot received for some other reason, the channel encoder at thetransmitter may apply a default MIMO MCS scheme, packet size andprecoding matrix. Alternatively, the channel encoder may apply theopen-loop implementation described above until feedback is received.

At block 612, the channel-encoded data (and rate-distortion informationon the source in the case of Op. A, as described in FIG. 4) aretransmitted to the receiver over the MIMO channel.

At block 614, the transmitted encoded data is reconstructed at thereceiver to provide a reconstructed source.

Block 616, (in the case of Op. B, as described in FIG. 4) a feedbackcomponent at the receiver determines the rate distortion informationfrom the source decoder.

At block 618, the feedback component also determines the optimal MIMOMCS scheme, packet size and precoding matrix. FIG. 7 provides an exampleof determining the optimal MIMO MCS scheme, packet size and precodingmatrix for minimizing overall end-to-end distortion.

At block 620, the link adaptation parameters, e.g., the optimal MIMO MCSscheme, packet size and precoding matrix are provided to the transmitteras feedback.

Process for Distortion-Aware Precoding

FIG. 7 illustrates a flow diagram of an example process 700 fordistortion-aware precoding to minimize end-to-end distortion in a MIMOsystem according to some implementations herein. In the flow diagram,the operations are summarized in individual blocks. The operations maybe performed in hardware, or as processor-executable instructions(software or firmware) that may be executed by a processor. Further, theprocess 700 may, but need not necessarily, be implemented using thesystems of FIGS. 3 and 4. Consequently, by way of explanation, and notlimitation, the process 700 is described in the context of the systemsof FIGS. 3 and 4.

At block 702, the SINR vector is initialized by the channel encoder. Forexample, each entry is an average received SINR value and represents thelong-term channel state information available at the transmitter.

At block 704, a set of CSI metrics is initialized for the statisticalinformation on the channel states. In some implementations, the exampleCSI models 1 or 2 discussed above may be used for initializing the CSImetrics. In these cases, the relevant metrics may be the channel mean(e.g., example CSI model 1) and/or transmit/receive correlation matrices(e.g., example CSI model 2).

At block 706, the optimal beamforming matrix is determined for the CSImetric of interest. For example, if the channel correlation structure isknown, then the optimal precoding beam directions may be obtained fromthe eigen-vectors of the transmit correlation matrix, i.e.,eigen-vectors of W_(T), as discussed above.

At block 708, distortion vectors are initialized for all MIMO MCSschemes, packet sizes and power allocations (i.e., across the beams).For example, each distortion vector may store distortion values atdifferent SINRs and different power allocations (across beams).

At block 710, for iterations over all SINR values and for a large numberof iterations over channel realizations, the MIMO channel realization His randomly generated as described above.

At block 712, the instantaneous received SINR for all MIMO MCS schemes,packet sizes and power allocations (across beams) is determined for eachSINR value and MIMO channel realization using the optimal beamformingmatrix corresponding to the considered CSI metric.

At block 714, the PER for all MIMO MCS schemes, packet sizes and powerallocations (across beams) is determined from the instantaneous receivedSINR (accounting for FEC type and code rates, differences among variousMIMO space-time modulations (e.g., STC vs. SM), and also accounting fororthogonal frequency-division multiplexing (OFDM) modulation, such as byusing physical layer (PHY) abstraction methodologies).

At block 716, for each MIMO MCS, packet size and power allocation(across beams), the end-to-end distortion is calculated based onequation (5) using the PER derived from the instantaneous received SINRin block 714. For instance, when example CSI model 1 is assumed, thenequation (7) is used to calculate the end-to-end distortion for eachMIMO MCS, packet size and power allocation. On the other hand, whenexample CSI model 2 is assumed then equation (9) is used to calculatethe end-to-end distortion for each MIMO MCS, packet size and powerallocation.

At block 718, the calculated distortion values (i.e., D_(ave)) arestored for all MIMO MCS schemes, packet sizes, power allocations (acrossbeams) and random channel realizations.

At block 720, blocks 710-718 are repeated for each SINR value until thelarge number of iterations over channel realizations have been completedand the distortion vectors are updated for all MIMO MCS schemes, packetsizes and power allocations (across beams). In particular, the entry ofeach vector corresponding to the particular SINR value is updated withthe mean distortion value (i.e., averaged over the random channelrealizations).

At block 722, when blocks 710-720 have been completed for iterationsover all SINRs, then, based on the distortion vectors for all MIMO MCSschemes, packet sizes and power allocations (across beams), the SINRthresholds are determined for switching between different MIMO MCSschemes, packet sizes and optimal power allocations (across beams) foreach SINR value using the criterion in equation (4) so that the averageend-to-end distortion is minimized at any SINR. When example CSI model 1is assumed, then the criterion in equations (6) and (7) may be used todetermine optimal MIMO MCS, packet size and power allocation. Whenexample CSI model 2 is assumed, the criterion in equations (8) and (9)may be used to determine optimal MIMO MCS, packet size and powerallocation. An optimal MIMO MCS scheme, packet size and precoding matrixQ can then be specified for a particular SINR based on known channelstate information. As discussed above in the examples of FIGS. 3-6, thechannel encoder space-time modulation component 320, 420 can apply theMIMO MCS scheme, packet size and the precoding component 322, 422 canapply the optimal precoding matrix during channel encoding of the sourcecontent being transmitted to the receiver.

The above precoding design criteria may be employed in conjunction withany distortion-rate function, and implementations herein also encompassany method for incorporating distortion criteria in the selection of aprecoding matrix. In this context, the selection of the precoding matrixmay include (i) beamforming to convert a MIMO channel into an equivalentsingle-input single-output (SISO) channel, (ii) precoded spatialmultiplexing, (iii) precoded orthogonal space-time block coding(OSTBCs), (iv) transmit power allocation and covariance optimizationacross space, time and frequency, and (v) transmit antenna selectiontechniques where M out of M_(t) transmit antennas are selected fortransmission.

In addition, implementations herein also encompass all link adaptationand precoding techniques toward optimizing objective distortion metricssuch as peak SNR (PSNR) and subjective distortion metrics that accountfor human visual perception and more precisely quantify a user'sperceived multimedia quality of experience (QoE), such as video qualitymetrics (VQM), structural similarity metrics (SSIM) and perceptualevaluation of video quality (PEVQ) metrics. Hence, the scope of thisdisclosure and associated rate-distortion characteristics underconsideration for MIMO link adaptation and precoding are based on thebroader definition of distortion to cover all objective and subjectivemultimedia quality metrics, tracked and specified for different sourceand channel coding rates. Moreover, overall multimedia distortion may bea function of PSNR, error-rate performance, and could be codec dependentand packet dependent (e.g., I-frames, P-frames, B-frames, etc.),including specific cost impacts of errors on specific types ofcompressed packets.

Applications

Implementations herein provide “distortion-awareness” and associatedjoint source-channel coding ideas to support link adaptation over MIMOsystems, where “distortion awareness” indicates that encoding parametersare optimized to reduce distortion in transmitted content. All of theMIMO link adaptation blocks at the transmitter and receiver, includingMIMO space-time modulation, packet size selection, MIMO precoding, FECouter coding and interleaving and feedback blocks are impacted andoperate differently under implementations of the distortion-aware MIMOlink adaptation framework herein. Furthermore, implementations hereinmay provide distortion-aware MIMO link adaptation techniques that areapplicable in conjunction with any of unicast (i.e., one streamingconnection established per user), broadcast (i.e., one streamingconnection established per service content) and multicast transmissiontechniques (i.e., one streaming connection established per a selectedgroup of users).

One example of an application according to some implementations of thedistortion-aware MIMO link adaptation techniques in this context may bemulticast broadcast services (MBS) in the WiMAX 802.16 standarddiscussed above, also known as multimedia broadcast and multicastservices (MBMS) in the standards developed by the Third GenerationPartnership Project (3GPP) and BroadCast and MultiCast Service (BCMCS)in standards developed by 3GPP2 (Third Generation Partnership Project2). For instance, in the context of MBS, conventional link adaptationapproaches that aim to maximize goodput typically determine themultimedia transmission rate so that a certain percentage (e.g., 95%) ofthe users in the network can reliably (e.g., with PER at 1% or lower)receive the multimedia transmission. However, according to someimplementations herein, the distortion-aware link adaptation protocolscan instead determine the multimedia transmission rate and theassociated level of multimedia reception quality (measured in terms ofPSNR or average end-to-end distortion) so that a certain percentage(e.g., 95%) of the users in the network can be guaranteed multimediaservice with a particular quality of experience (i.e., PSNR or averageend-to-end distortion below a predetermined threshold).

Example System

FIG. 8 illustrates an example of a system 800 for minimizing end-to-enddistortion using distortion-aware MIMO link adaptation according to someimplementations. To this end, the system 800 includes a transmitter 802configured to communicate wirelessly with a receiver 804 over a MIMOchannel 806. Transmitter 802 includes a plurality of transmitterantennas 808 for MIMO communication with a plurality of receiverantennas 810 at receiver 804. Transmitter 802 also includes atransmitter circuit or device 812, such as a radio front end or otherwireless transmission mechanism for transmitting signals over the MIMOchannel 806. Similarly, receiver 804 may include a receiver circuit ordevice 814, such as a radio front end or other wireless receivingmechanism for receiving the signals from transmitter 802. In addition,transmitter 802 may include one or more processors 816 coupled to amemory 818 or other processor-readable storage media. For example,memory 818 may contain a distortion awareness component 820 able to beexecuted by the one or more processors 816 to cause transmitter 802 tocarry out the functions described above for minimizing end-to-enddistortion. Similarly, receiver 804 may include one or more processors822 coupled to a memory 824 or other processor-readable storage media.Memory 824 may contain a distortion awareness component 826 able to beexecuted by the one or more processors 822 to cause receiver 804 tocarry out the functions described above for minimizing end-to-enddistortion, such as providing feedback during the closed-loopimplementations.

In some implementations, the processor(s) 816, 822 can be a singleprocessing unit or a number of processing units, all of which mayinclude multiple computing units or multiple cores. The processor(s)816, 822 may be implemented as one or more microprocessors,microcomputers, microcontrollers, digital signal processors, centralprocessing units, state machines, logic circuitries, and/or any devicesthat manipulate signals based on operational instructions. Among othercapabilities, the processors 816, 822 can be configured to fetch andexecute processor-executable instructions stored in the memories 818,824, respectively, or other processor-readable storage media.

The memories 818, 824 can include any processor-readable storage mediaknown in the art including, for example, volatile memory (e.g., RAM)and/or non-volatile memory (e.g., flash, etc.), mass storage devices,such as hard disk drives, solid state drives, removable media, includingexternal drives, removable drives, floppy disks, optical disks, othernon-transitory storage media, or the like, or any combination thereof.The memories 818, 824 store computer-readable processor-executableprogram instructions as computer program code that can be executed bythe processors 816, 822, respectively, as a particular machine forcarrying out the methods and functions described in the implementationsherein. Further, memories 818, 824 may also include other programmodules stored therein and executable by processor(s) 818, 822,respectively, for carrying out implementations herein, such codecs, orthe like. For example, memory 818 may include a source encoder 828 and achannel encoder 830, as discussed above. Channel encoder may include aspace-time modulation component 832 and a precoding component 834implemented by processor 816 to perform ST modulation and precoding,respectively, according to the framework for optimal design of theprecoding matrix, as described above. The distortion awareness component820 may be incorporated into the channel encoder 830, or may be embodiedas a separate component. Further, memory 824 on receiver 804 may includea source decoder 836 and a space-time decoder 838, for executing thefunctions discussed above. Memories 818, 824 may also include datastructures, such as stored SNR vectors, lookup tables, default MIMO MCSschemes, packet sizes and precoding matrices, and the like (not shown),as discussed above.

Additionally, transmitter 802 and receiver 804 may be implemented in avariety of devices and systems, such as cellular communications systems,Wi-Fi systems, or the like. For example, transmitter 802 might beincorporated in a mobile computing device, such as a cell phone, smartphone, laptop or the like, while receiver 804 might be implemented in acell tower, wireless access point, a second computing device, or thelike, or vice versa. Further, while example system architectures havebeen described, it will be appreciated that other implementations arenot limited to the particular system architectures described herein. Forexample the techniques and architectures described herein may inincorporated in any of a variety of wireless communication devices, andimplementations herein are not limited to any type of communicationdevices.

CONCLUSION

Although the subject matter has been described in language specific tostructural features and/or methodological acts, this disclosure isintended to cover any and all adaptations or variations of the disclosedimplementations, and the following claims should not be construed tolimit this patent to the specific implementations disclosed in thespecification. Instead, the scope of this document is to be determinedentirely by the following claims, along with the full range ofequivalents to which such claims are entitled.

1. A method comprising: selecting, by a processor, a precoding matrix touse for channel encoding of a source, the precoding matrix beingselected based on considerations for minimizing distortion between thesource and a reconstructed source induced by transmission of the sourceover a multiple input multiple output (MIMO) channel; and performingprecoding on the source using the selected precoding matrix to providechannel-encoded data for transmitting the source over the MIMO channel.2. The method according to claim 1, further comprising: selecting theone or more parameters based on a determined minimum average end-to-enddistortion, wherein the minimum average end-to-end distortion isdetermined as a function of packet error rate for the MIMO channel. 3.The method according to claim 1, wherein the one or more parametersselected for minimizing distortion comprise a MIMO modulation and codingscheme.
 4. The method according to claim 3, wherein selection of theMIMO modulation and coding scheme further comprises at least one of:selection of the modulation order; selection of the forward errorcorrection type and coding rate; or determination of space-timemodulation techniques to be used.
 5. The method according to claim 1,wherein the one or more parameters comprise a precoding matrix.
 6. Themethod according to claim 5, wherein selecting the precoding matrixfurther comprises at least one of: beamforming to convert a MIMO channelinto an equivalent single-input single-output channel; precoded spatialmultiplexing; precoded orthogonal space-time block coding; transmitpower allocation and covariance optimization; or transmit antennaselection techniques.
 7. The method according to claim 1, furthercomprising: performing source encoding on the source with a sourceencoder prior to the channel encoding; receiving rate distortioninformation from the source encoder, wherein the rate distortioninformation is used for selecting the one or more parameters forminimizing the distortion.
 8. The method according to claim 1, whereinthe one or more parameters are MIMO link adaptation parameters receivedby a transmitter performing the channel encoding as feedback from areceiver receiving data from the transmitter; wherein the receiverdetermines the one or more parameters based upon the data received fromthe transmitter.
 9. The method according to claim 8, wherein thereceiver determines the one or more parameters based at least in part onrate distortion information; wherein the rate distortion information isdetermined at the receiver or sent by the transmitter to the receiver.10. Non-transitory processor-readable storage media containingprocessor-executable instructions to be executed by a processor forcarrying out the method according to claim
 1. 11. A system comprising: atransmitter having a processor to implement a channel encoder forencoding a source for transmission over a multiple input multiple output(MIMO) channel; and a precoding component that precodes the source usinga precoding matrix selected to minimize distortion between the sourceand a reconstructed source induced by transmission of the source overthe MIMO channel.
 12. The system according to claim 11, wherein theselection of the MIMO MCS and precoding matrix is performed by areceiver in communication with the transmitter, wherein the transmitterreceives the selected MIMO MCS and precoding matrix from the receiver.13. The system according to claim 11, wherein the channel encoderselects the MIMO MCS and precoding matrix to minimize distortion basedupon at least one of instantaneously determined channel conditions orstatistically known channel conditions.
 14. The system according toclaim 11, wherein the source is a continuous multimedia source to betransmitted to a receiver to provide a reconstructed source at thereceiver, wherein the MIMO MCS and precoding matrix selected to minimizedistortion are selected based on a determined average end-to-enddistortion between the source and the reconstructed source, wherein theaverage end-to-end distortion is determined based at least in part upona calculated packet error rate for the MIMO channel.
 15. The systemaccording to claim 11, wherein the transmitter further comprises asource encoder for encoding the source prior to encoding by the channelencoder, wherein the source encoder provides rate distortion informationto the channel encoder for use by the channel encoder in selecting theMIMO MCS and precoding matrix.